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Remote Ai Data Collection Jobs in Arizona (NOW HIRING)

Initio Capital

Software Market Analyst

Phoenix, AZ · Remote

... AI/data, business operations, corporate development, private markets, or entrepreneurship. COMPENSATION Compensation varies by project and experience. Many roles are flexible, remote, and project ...

Initio Capital

B2B Strategy Research Associate

Phoenix, AZ · Remote

... AI/data, business operations, corporate development, private markets, or entrepreneurship. COMPENSATION Compensation varies by project and experience. Many roles are flexible, remote, and project ...

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Phoenix Staff

Lead Data & AI Engineer

Phoenix, AZ · On-site +1

$50 - $60/hr

Phoenix, AZ (hybrid remote) Type: 6-month contract to hire Pay: $50-60/hr We're looking for a Lead Data & AI Engineer to lead the design and delivery of secure, scalable data and AI solutions within ...

micro1 AI

AI Trainer - Microbiology Expert

Peoria, AZ · Remote

$70 - $90/hr

Remote micro1 is engaging Microbiologists to contribute their scientific expertise to a unique ... Document experimental findings and processes with a focus on clarity for AI training data.

micro1 AI

AI Trainer - Microbiology Expert

Buckeye, AZ · Remote

$70 - $90/hr

Remote micro1 is engaging Microbiologists to contribute their scientific expertise to a unique ... Document experimental findings and processes with a focus on clarity for AI training data.

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All JobsAi Data Collection JobsRemote Ai Data Collection JobsRemote Ai Data Collection Jobs in Arizona

Remote Ai Data Collection information

What skills and qualifications are needed for a remote AI data collection specialist?

To thrive as a Remote AI Data Collection Specialist, you need attention to detail, data management skills, and a basic understanding of machine learning concepts, often supported by a degree in computer science or related fields. Familiarity with data annotation tools, spreadsheets, and platforms like Labelbox or Amazon SageMaker is commonly required. Strong communication, time management, and problem-solving skills are important for collaborating remotely and meeting project deadlines. These abilities ensure accurate, efficient data gathering and annotation, which are critical for the quality and reliability of AI model development.

What is remote AI data collection?

Remote AI data collection refers to the process of gathering and labeling data—such as images, audio, text, or video—from various sources using digital tools, often from a remote location. This data is used to train and improve artificial intelligence and machine learning models. People working in this field can perform tasks like annotating images, transcribing audio, or categorizing text, all from their home or another remote setting. The work is essential for creating accurate AI systems and often offers flexible hours. It usually requires basic computer skills and attention to detail.

What remote jobs can I do with AI?

Remote AI data collection jobs involve gathering and labeling data to train machine learning models, often requiring skills in data annotation, understanding of AI tools, and attention to detail. These roles can include image, text, or audio data labeling and are typically performed using specialized platforms or software, offering flexible schedules for qualified candidates.

What is the difference between Remote Ai Data Collection vs Remote Data Annotator?

AspectRemote Ai Data CollectionRemote Data Annotator
Required CredentialsBasic computer skills, training in data collection toolsAttention to detail, familiarity with annotation software
Work EnvironmentRemote, flexible hours, often on mobile or desktopRemote, flexible hours, often on desktop or specialized platforms
Industry UsageAI training data gathering across various sectorsLabeling and annotating data for machine learning models
Common Search IntentJobs involving data collection for AIJobs focused on data labeling and annotation

Remote Ai Data Collection involves gathering raw data for AI training, often requiring basic technical skills. Remote Data Annotator focuses on labeling and annotating data to improve machine learning models. Both roles are remote, but they differ in tasks and skill requirements, serving different stages of AI data preparation.

What are common challenges in a remote AI data collection role, and how can they be managed?

A common challenge in Remote AI Data Collection roles is ensuring data quality and consistency, especially when working independently without direct supervision. It is important to follow detailed guidelines precisely and communicate proactively with project managers or team leads whenever uncertainties arise. Time management and maintaining motivation can also be challenging when working remotely, so setting a structured schedule and leveraging collaboration tools can help. Regular check-ins with the team and staying updated with project requirements are key to overcoming these challenges and delivering reliable results.

How to be a Remote AI Data Collection?

To work as a remote AI data collector, you should have strong attention to detail, good communication skills, and familiarity with data annotation tools or platforms. Many roles require basic computer skills and the ability to follow specific instructions, often with flexible schedules. Gaining experience in data labeling or annotation can improve your chances of securing such positions.
What are the most commonly searched types of Ai Data Collection jobs in Arizona? The most popular types of Ai Data Collection jobs in Arizona are:
What are popular job titles related to Remote Ai Data Collection jobs in Arizona? For Remote Ai Data Collection jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Remote Ai Data Collection jobs in Arizona look for? The top searched job categories for Remote Ai Data Collection jobs in Arizona are:
What cities in Arizona are hiring for Remote Ai Data Collection jobs? Cities in Arizona with the most Remote Ai Data Collection job openings:
Remote Ai Data Collection jobs near you

Principal AI Data Scientist

MSR Technology Group

Phoenix, AZ • Remote

Full-time

Re-posted 24 days ago

Job description


Infomatics is partnered with a large retailer that is hiring a Principal AI Data Scientist on a direct hire/FTE basis near Phoenix, AZ. Can work remote. All applicants must be eligible & willing to be hired on W2.

You will lead various AI efforts involving computer vision, deep learning, and nlp in addition to other machine learning model builds. You will not only work on large scale projects to provide value to the customers but are also routinely involved in building our internal R&D capability to have an edge in the analytics industry. You will lead some of the most strategic and very complex problems.
Duties/Responsibilities:
  • Builds and validates machine learning models of high risk/reward problems utilizing large scale data from multiple data sources and methodologies.
  • Uses machine learning techniques to create data-driven solutions for various business use-cases.
  • Writes programs utilizing existing libraries and methodologies.
  • Interprets, communicates, and presents analytic results to C-Level executives and below.
  • Consistently collaborates with fellow data scientists, data engineers, business partners, project managers, cross-functional teams, key stakeholders, and other domains to drive business value.
  • Leads AI best practice sharing opportunities and knowledge of industry trends and innovations in data science.
  • Leads projects with external partners and vendors to develop solutions to meet business needs while resolving any issues that may arise.
  • Contributes to the organization's data strategy and roadmap.
  • Embeds and drives the organization with the most up-to-date AI methodology.
Qualifications:
  • Master's or PhD degree in a quantitative field with 5+ years of data science experience.
  • Applied expertise in artificial intelligence with experience applying natural language processing, computer vision (image processing), and deep leaning. Need to have the capability to leverage current mature mainstream AI application tools and methodology
  • Proficiency in machine learning with familiarity and actual applications of scikit-learn library machine learning techniques such as decision tree, gradient boosting, XGBoost, etc. for regression, classification, or segmentation problems.
  • Programming expertise in Python with familiarity with cloud environments (AWS, Databricks, etc.)
  • Ability to work with large data sets from multiple data sources
  • Ability to communicate complex analytics concepts and techniques to C-Level executives and below
  • Ability to work collaboratively with other data scientists, data engineers, multiple stakeholders across the business, and with external partners
  • Intellectual curiosity, a passion for data, and a results orientation.